Introduction: Cardiovascular disease nursing is a critical clinical application that necessitates real-time monitoring models. Previous models required the use of multi-lead signals and could not be customized as needed. Traditional methods relied on manually designed supervised algorithms, based on empirical experience, to identify waveform abnormalities and classify diseases, and were incapable of monitoring and alerting abnormalities in individual waveforms. Methods: This research reconstructed the vector model for arbitrary leads using the phase space-time-delay method, enabling the model to arbitrarily combine signals as needed while possessing adaptive denoising capabilities. After employing automatically constructed machine learning algorithms and designing for rapid convergence, the model can identify abnormalities in individual waveforms and classify diseases, as well as detect and alert on abnormal waveforms. Result: Effective noise elimination was achieved, obtaining a higher degree of loss function fitting. After utilizing the algorithm in Section 3.1 to remove noise, the signal-to-noise ratio increased by 8.6%. A clipping algorithm was employed to identify waveforms significantly affected by external factors. Subsequently, a network model established by a generative algorithm was utilized. The accuracy for healthy patients reached 99.2%, while the accuracy for APB was 100%, for LBBB 99.32%, for RBBB 99.1%, and for P-wave peak 98.1%. Conclusion: By utilizing a three-dimensional model, detailed variations in electrocardiogram signals associated with different diseases can be observed. The clipping algorithm is effective in identifying perturbed and damaged waveforms. Automated neural networks can classify diseases and patient identities to facilitate precision nursing.

1.
Gadaleta
M
,
Harrington
P
,
Barnhill
E
,
Hytopoulos
E
,
Turakhia
MP
,
Steinhubl
SR
, et al
.
Prediction of atrial fibrillation from at-home single-lead ECG signals without arrhythmias
.
NPJ Digit Med
.
2023
;
6
(
1
):
229
.
2.
Carrarini
C
,
Di Stefano
V
,
Russo
M
,
Dono
F
,
Di Pietro
M
,
Furia
N
, et al
.
ECG monitoring of post-stroke occurring arrhythmias: an observational study using 7-day Holter ECG
.
Sci Rep
.
2022
;
12
(
1
):
228
.
3.
Chen
J
,
Chen
Z
,
Li
C
,
Yang
K
,
Li
X
,
Jiang
J
, et al
.
Preprocessing and pattern recognition for Single-Lead cardiac dynamic model
.
Biomed Signal Process Control
.
2023
;
82
:
104544
.
4.
Deng
M
,
Wang
C
,
Tang
M
,
Zheng
T
.
Extracting cardiac dynamics within ECG signal for human identification and cardiovascular diseases classification
.
Neural Netw
.
2018
;
100
:
70
83
.
5.
Lai
J
,
Tan
H
,
Wang
J
,
Ji
L
,
Guo
J
,
Han
B
, et al
.
Practical intelligent diagnostic algorithm for wearable 12-lead ECG via self-supervised learning on large-scale dataset
.
Nat Commun
.
2023
;
14
(
1
):
3741
.
6.
Guo
J
Electrocardiography
. 1st ed223.
Beijing
:
People’s Medical Publishing House
;
2002
. [郭继鸿. “心电图学.” 第 l 版. 北京: 人民卫生出版社 223 (2002)].
7.
Chowdhury
MH
,
Cheung
RCC
.
Reconfigurable architecture for multi-lead ecg signal compression with high-frequency noise reduction
.
Sci Rep
.
2019
;
9
(
1
):
17233
.
8.
Nguyen
P
,
Kim
J-M
.
Adaptive ECG denoising using genetic algorithm-based thresholding and ensemble empirical mode decomposition
.
Inf Sci
.
2016
;
373
:
499
511
.
9.
Deng
M
,
Wang
C
,
Tang
M
,
Zheng
T
.
Extracting cardiac dynamics within ECG signal for human identification and cardiovascular diseases classification
.
Neural Netw
.
2018
;
100
:
70
83
.
10.
Raghunath
S
,
Ulloa Cerna
AE
,
Jing
L
,
vanMaanen
DP
,
Stough
J
,
Hartzel
DN
, et al
.
Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network
.
Nat Med
.
2020
;
26
(
6
):
886
91
.
11.
Raj
S
,
Ray
KC
.
A personalized point-of-care platform for real-time ECG monitoring
.
IEEE Trans Consumer Electron
.
2018
;
64
(
4
):
452
60
.
12.
Goldberger
AL
,
Amaral
LA
,
Glass
L
,
Hausdorff
JM
,
Ivanov
PC
,
Mark
RG
, et al
.
PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals
.
Circulation
.
2000
;
101
(
23
):
e215
20
.
13.
Němcová
A
,
Smíšek
R
,
Maršánová
L
,
Smital
L
,
Vítek
M
.
A comparative analysis of methods for evaluation of ECG signal quality after compression
.
BioMed Res Int
.
2018
;
2018
:
1868519
.
14.
Moody
GB
,
Mark
RG
.
The impact of the MIT-BIH arrhythmia database
.
IEEE Eng Med Biol Mag
.
2001
;
20
(
3
):
45
50
.
15.
Haykin
S
.
Neural networks and learning machines, 3./E
.
Pearson Education India
;
2009
.
16.
Chun-Liang
L
,
Wei-Cheng
C
,
Yu
C
,
Yiming
Y
,
Barnabás
P
. “Mmd gan: Towards deeper understanding of moment matching network.” Advances in neural information processing systems 30 (2017).
17.
Choi
JW
,
Hong
DY
,
Jung
C
,
Hwang
E
,
Park
SH
,
Roh
SY
.
A multi-view learning approach to enhance automatic 12-lead ECG diagnosis performance
.
Biomed Signal Process Control
.
2024
;
93
:
106214
.
18.
Wang
LH
,
Zou
YY
,
Xie
CX
,
Yang
T
,
Abu
PAR
.
Feasibility and validity of using deep learning to reconstruct 12-lead ECG from three-lead signals
.
J Electrocardiol
.
2024
;
84
:
27
31
.
19.
Islam
MR
,
Qaraqe
M
,
Qaraqe
K
,
Serpedin
E
.
CAT-Net: convolution, attention, and transformer based network for single-lead ECG arrhythmia classification
.
Biomed Signal Process Control
.
2024
;
93
:
106211
.
20.
Allam
JP
,
Sahoo
SP
,
Ari
S
.
Multi-stream Bi-GRU network to extract a comprehensive feature set for ECG signal classification
.
Biomed Signal Process Control
.
2024
;
92
:
106097
.
21.
Avetisyan
A
,
Tigranyan
S
,
Asatryan
A
,
Mashkova
O
,
Skorik
S
,
Ananev
V
, et al
.
Deep neural networks generalization and fine-tuning for 12-lead ECG classification
.
Biomed Signal Process Control
.
2024
;
93
:
106160
.
22.
Narotamo
H
,
Dias
M
,
Santos
R
,
Carreiro
AV
,
Gamboa
H
,
Silveira
M
.
Deep learning for ECG classification: a comparative study of 1D and 2D representations and multimodal fusion approaches
.
Biomed Signal Process Control
.
2024
;
93
:
106141
.
23.
Carll
AP
,
Salatini
R
,
Pirela
SV
,
Wang
Y
,
Xie
Z
,
Lorkiewicz
P
, et al
.
Inhalation of printer-emitted particles impairs cardiac conduction, hemodynamics, and autonomic regulation and induces arrhythmia and electrical remodeling in rats
.
Part Fibre Toxicol
.
2020
;
17
(
1
):
7
.
24.
Carll
AP
,
Arab
C
,
Salatini
R
,
Miles
MD
,
Nystoriak
MA
,
Fulghum
KL
, et al
.
E-cigarettes and their lone constituents induce cardiac arrhythmia and conduction defects in mice
.
Nat Commun
.
2022
;
13
(
1
):
6088
.
25.
Kucera
C
,
Ramalingam
A
,
Srivastava
S
,
Bhatnagar
A
,
Carll
AP
.
Nicotine formulation influences the autonomic and arrhythmogenic effects of electronic cigarettes
.
Nicotine Tob Res
.
2024
;
26
(
5
):
536
44
.
26.
Irfan
AB
,
Arab
C
,
DeFilippis
AP
,
Lorkiewicz
P
,
Keith
RJ
,
Xie
Z
, et al
.
Smoking accelerates atrioventricular conduction in humans concordant with increased dopamine release
.
Cardiovasc Toxicol
.
2021
;
21
(
2
):
169
78
.
27.
Conklin
DJ
,
Schick
S
,
Blaha
MJ
,
Carll
A
,
DeFilippis
A
,
Ganz
P
, et al
.
Cardiovascular injury induced by tobacco products: assessment of risk factors and biomarkers of harm. A Tobacco Centers of Regulatory Science compilation
.
Am J Physiol Heart Circ Physiol
.
2019
;
316
(
4
):
H801
27
.
28.
Kucera
C
,
Ramalingam
A
,
Srivastava
S
,
Bhatnagar
A
,
Carll
AP
.
Nicotine formulation influences the autonomic and arrhythmogenic effects of electronic cigarettes
.
Nicotine Tob Res
.
2024
;
26
(
5
):
536
44
.
29.
da Silva
TD
,
Massetti
T
,
Crocetta
TB
,
de Mello Monteiro
CB
,
Carll
A
,
Vanderlei
LCM
, et al
.
Heart rate variability and cardiopulmonary dysfunction in patients with duchenne muscular dystrophy: a systematic review
.
Pediatr Cardiol
.
2018
;
39
(
5
):
869
83
.
30.
Sadeghi
Z
,
Alizadehsani
R
,
Cifci
MA
,
Kausar
S
,
Rehman
R
,
Mahanta
P
,
Pardalos
PM
.
A brief review of explainable artificial intelligence in healthcare
;
2023
. arXiv preprint arXiv:2304.01543.
31.
Zhang
Z
,
Chen
L
,
Xu
P
,
Hong
Y
.
Predictive analytics with ensemble modeling in laparoscopic surgery: a technical note
.
Laparosc Endoscopic Robotic Surg
.
2022
;
5
(
1
):
25
34
.
32.
Abdar
M
,
Nasarian
E
,
Zhou
X
,
Bargshady
G
,
Wijayaningrum
VN
,
Hussain
S
.
Performance improvement of decision trees for diagnosis of coronary artery disease using multi filtering approach
.
2019 IEEE 4th international conference on computer and communication systems (ICCCS)
.
IEEE
;
2019
. p.
26
30
.
33.
Alizadehsani
R
,
Habibi
J
,
Hosseini
MJ
,
Mashayekhi
H
,
Boghrati
R
,
Ghandeharioun
A
, et al
.
A data mining approach for diagnosis of coronary artery disease
.
Comput Methods Programs Biomed
.
2013
;
111
(
1
):
52
61
.
34.
Alizadehsani
R
,
Zangooei
MH
,
Hosseini
MJ
,
Habibi
J
,
Khosravi
A
,
Roshanzamir
M
, et al
.
Coronary artery disease detection using computational intelligence methods
.
Knowl Base Syst
.
2016
;
109
:
187
97
.
35.
Alizadehsani
R
,
Roshanzamir
M
,
Sani
Z
.
Extention of Z-Alizadeh sani dataset
.
UCI Machine Learn Repository
.
2017
.
36.
Arabasadi
Z
,
Alizadehsani
R
,
Roshanzamir
M
,
Moosaei
H
,
Yarifard
AA
.
Computer aided decision making for heart disease detection using hybrid neural network-Genetic algorithm
.
Comput Methods Programs Biomed
.
2017
;
141
:
19
26
.
37.
Al-Zaiti
SS
,
Martin-Gill
C
,
Zègre-Hemsey
JK
,
Bouzid
Z
,
Faramand
Z
,
Alrawashdeh
MO
, et al
.
Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction
.
Nat Med
.
2023
;
29
(
7
):
1804
13
.
38.
Jin
S
,
Chen
L
,
Chen
K
,
Hu
C
,
Hu
SA
,
Zhang
Z
.
Establishment of a Chinese critical care database from electronic healthcare records in a tertiary care medical center
.
Sci Data
.
2023
;
10
(
1
):
49
.
39.
Zhang
Z
,
Chen
L
,
Zhang
H
,
Xiao
W
,
Yang
J
,
Huang
J
, et al
.
Genetic correlations and causal relationships between cardio-metabolic traits and sepsis
.
Sci Rep
.
2024
;
14
(
1
):
5718
.
40.
Zhang
Z
,
Chen
L
,
Xu
P
,
Wang
Q
,
Zhang
J
,
Chen
K
, et al
.
Effectiveness of automated alerting system compared to usual care for the management of sepsis
.
NPJ Digit Med
.
2022
;
5
(
1
):
101
.
41.
Nasirzadeh
F
,
Mir
M
,
Hussain
S
,
Tayarani Darbandy
M
,
Khosravi
A
,
Nahavandi
S
, et al
.
Physical fatigue detection using entropy analysis of heart rate signals
.
Sustainability
.
2020
;
12
(
7
):
2714
.
42.
Alizadehsani
R
,
Oyelere
SS
,
Hussain
S
,
Jagatheesaperumal
SK
,
Calixto
RR
,
Rahouti
M
, et al
.
Explainable artificial intelligence for drug discovery and development-A comprehensive survey
.
IEEE Access
.
2024
;
12
:
35796
812
.
43.
Che
Q
,
Song
T
,
Liang
N
,
Guo
J
,
Chen
Z
,
Liu
X
, et al
.
Dazhu hongjingtian injection for ischemic stroke: protocol for a prospective, multicenter observational study
.
JMIR Res Protoc
.
2023
;
12
:
e52447
.
44.
Sharifrazi
D
,
Alizadehsani
R
,
Hoseini Izadi
N
,
Roshanzamir
M
,
Shoeibi
A
,
Khozeimeh
F
, et al
.
Hypertrophic cardiomyopathy diagnosis based on cardiovascular magnetic resonance using deep learning techniques
.
Colour Filtering
;
2021
.
You do not currently have access to this content.